object
softmax extends UFunc
Type Members
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type
Impl[V, VR] = UImpl[softmax.this.type, V, VR]
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type
Impl2[V1, V2, VR] = UImpl2[softmax.this.type, V1, V2, VR]
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type
Impl3[V1, V2, V3, VR] = UImpl3[softmax.this.type, V1, V2, V3, VR]
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type
Impl4[V1, V2, V3, V4, VR] = UImpl4[softmax.this.type, V1, V2, V3, V4, VR]
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Value Members
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final
def
!=(arg0: AnyRef): Boolean
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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final
def
apply[V1, V2, V3, V4, VR](v1: V1, v2: V2, v3: V3, v4: V4)(implicit impl: Impl4[V1, V2, V3, V4, VR]): VR
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final
def
apply[V1, V2, V3, VR](v1: V1, v2: V2, v3: V3)(implicit impl: Impl3[V1, V2, V3, VR]): VR
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final
def
apply[V1, V2, VR](v1: V1, v2: V2)(implicit impl: Impl2[V1, V2, VR]): VR
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final
def
apply[V, VR](v: V)(implicit impl: Impl[V, VR]): VR
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def
array(arr: Array[Double], length: Int): Double
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final
def
asInstanceOf[T0]: T0
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implicit
def
canZipMapValuesImpl[T, V1, VR, U](implicit handhold: HandHold[T, V1], impl: Impl2[V1, V1, VR], canZipMapValues: CanZipMapValues[T, V1, VR, U]): Impl2[T, T, U]
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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implicit
object
implDoubleDouble extends Impl2[Double, Double, Double]
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final
def
inPlace[V, V2, V3](v: V, v2: V2, v3: V3)(implicit impl: generic.UFunc.InPlaceImpl3[softmax.this.type, V, V2, V3]): Unit
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final
def
inPlace[V, V2](v: V, v2: V2)(implicit impl: generic.UFunc.InPlaceImpl2[softmax.this.type, V, V2]): Unit
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final
def
isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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implicit
def
reduceDouble[T](implicit iter: CanTraverseValues[T, Double], maxImpl: max.Impl[T, Double]): Impl[T, Double]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
Inherited from AnyRef
Inherited from Any
Computes the softmax (a.k.a. logSum) of an object. Softmax is defined as \log \sum_i \exp(x(i)), but implemented in a more numerically stable way. Softmax is so-called because it is a differentiable function that tends to look quite a lot like max. Consider log(exp(30) + exp(10)). That's basically 30. We use softmax a lot in machine learning.